This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on data collected through sensors embedded in smartphones. The data include date, time, latitude, longitude, altitude, and speed, gathered using a mobile application specifically designed for this project. These data were collected through the smartphone’s GPS to enhance the accuracy of the analysis. The stopping times of each transport mode, as well as the distance traveled and average speed, are analyzed to identify patterns and distinctive features. Conducted in Cuenca, Ecuador, the study aims to develop and validate an algorithm to enhance urban planning. It extracts significant features from mobility patterns, including speed, acceleration, and over-acceleration, and applies longitudinal dynamics to train the classification model. The classification algorithm relies on a decision tree model, achieving a high accuracy of 94.6% in validation and 94.9% in testing, demonstrating the effectiveness of the proposed approach. Additionally, the precision metric of 0.8938 signifies the model’s ability to make correct positive predictions, with nearly 90% of positive instances correctly identified. Furthermore, the recall metric at 0.83084 highlights the model’s capability to identify real positive instances within the dataset, capturing over 80% of positive instances. The calculated F1-score of 0.86117 indicates a harmonious balance between precision and recall, showcasing the models robust and well-rounded performance in classifying transport modes effectively. The study discusses the potential applications of this method in urban planning, transport management, public transport route optimization, and urban traffic monitoring. This research represents a preliminary stage in generating an origin–destination (OD) matrix to better understand how people move within the city.

This paper presents a methodology for estimating polluting emissions in the city of Cuenca, Ecuador. Currently, the polluting emissions inventory ignores several important factors in the generation of emissions produced by mobile sources. The COPERT emissions inventory model does not consider the speed profile of the vehicles and the tests carried out in the vehicle technical inspection centers are carried out empty and without any opposing load to the engine. For this reason, it is proposed that the vehicles be tested in dynamic conditions according to the EURO 6 regulations and the implementation of the real emissions cycle, allowing to obtain driving characteristics and values that are closer to reality. Subsequently, the model is adjusted using neural networks and the estimation of pollutants for the period 2008 - 2018 is carried out. According to the data found, the average percentage of error is 35.26% between the estimates made according to the EURO 6 standard and the values of the COPERT III emissions inventory.

Urban sustainable mobility requires understanding how people travel, which modes they use, and what impacts these choices generate. This study proposes a smart mobility analytics framework that integrates GPS traces, dynamic traffic variables, and machine learning to infer transport modes and sustainability metrics in Cuenca, Ecuador. Geospatial and kinematic data were collected at 1 Hz from 50 participants over four working weeks, yielding 8.99 million samples across five modes: walking, cycling, tram, bus, and private vehicles. A compact subset of physical and spatial predictors, derived from speed, acceleration, jerk, longitudinal forces, and distance to public transport routes, was selected using the Football Optimization Algorithm. A classification tree trained with a 70/15/15 train–validation–test split achieved an overall accuracy of 84.2%, with class precisions of about 99% for pedestrian and bicycle, 93% for tram, 76% for private vehicles, and 64% for bus. The classified trajectories show that walking and cycling account for approximately 65% of total travel time but only 2% of total distance and 1.7% of CO₂ emissions, whereas motorized modes generate more than 98% of emissions. Buses contribute nearly four times more CO₂ than private vehicles, despite carrying a larger passenger volume. The proposed framework delivers detailed, policy-relevant indicators to support low-carbon urban transport strategies.

This study evaluates the feasibility of electrifying the urban public transport system in Cuenca, Ecuador, through an approach that combines real data acquisition, dynamic-energy modeling, and optimization. Position (latitude, longitude, altitude) and speed data were collected across 35 urban routes to calculate longitudinal forces, energy consumption, and the state of charge of a reference electric bus. Based on this analysis, the avoided $\text{CO}_{2}$ emissions resulting from replacing diesel buses were estimated. To optimize the allocation of electric units, the Football Optimization Algorithm was implemented, formulated as a maximization problem of avoided $\text{CO}_{2}$ under capital investment and fleet-size constraints. Results show that the electrification of 150 buses, with an investment of USD 60 million, can reduce approximately 467 kg of $\text{CO}_{2}$ per day, prioritizing high-consumption routes such as lines 16, 21, and 27. The FOA demonstrated fast and stable convergence in fewer than 100 iterations, strategically distributing units to maximize environmental impact and cost-effectiveness. The proposed methodology provides a replicable framework for other Latin American cities with similar geographic and operational conditions, supporting decision-making for sustainable urban mobility.